Czyz Marcin, Radwan Hesham, Li Jian Y, Filippi Christopher G, Tykocki Tomasz, Schulder Michael
Hofstra North Shore LIJ School of Medicine, Manhasset, New York, USA.
Walton Centre NSH Trust, Liverpool, UK.
Neurosurgery. 2017 Feb 1;80(2):300-308. doi: 10.1093/neuros/nyw030.
There is no objective and readily accessible method for the preoperative determination of atypical characteristics of a meningioma grade.
To evaluate the feasibility of using fractal analysis as an adjunctive tool to conventional radiological techniques in visualizing histopathological features of meningiomas.
A group of 27 patients diagnosed with atypical (WHO grade II) meningioma and a second group of 27 patients with benign (WHO grade I) meningioma were enrolled in the study. Preoperative brain magnetic resonance (MR) studies (T1-wieghted, post-gadolinium) were processed and analyzed to determine the average fractal dimension (FDa) and maximum fractal dimension (FDm) of the contrast-enhancing region of the tumor using box-count method. FDa and FDm as well as particular radiological features were included in the logistic regression model as possible predictors of malignancy.
The cohort consisted of 34 women and 20 men, mean age of 62 ± 15 yr. Fractal analysis showed good interobserver reproducibility (Kappa >0.70). Both FDa and FDm were significantly higher in the atypical compared to the benign meningioma group (P < .0001). Multivariate logistic regression model reached statistical significance with P = .0001 and AUC = 0.87. The FDm, which was greater than 1.31 (odds ratio [OR], 12.30; P = .039), and nonskull base localization (OR, .052; P = .015) were confirmed to be statistically significant predictors of the atypical phenotype.
Fractal analysis of preoperative MR images appears to be a feasible adjunctive diagnostic tool in identifying meningiomas with potentially aggressive clinical behavior.
目前尚无客观且易于获取的方法用于术前判定脑膜瘤分级的非典型特征。
评估将分形分析作为传统放射学技术的辅助工具来显示脑膜瘤组织病理学特征的可行性。
本研究纳入了一组27例诊断为非典型（世界卫生组织II级）脑膜瘤的患者以及另一组27例良性（世界卫生组织I级）脑膜瘤患者。对术前脑部磁共振（MR）研究（T1加权、钆增强后）进行处理和分析，采用盒计数法确定肿瘤强化区域的平均分形维数（FDa）和最大分形维数（FDm）。FDa、FDm以及特定的放射学特征被纳入逻辑回归模型，作为恶性肿瘤的可能预测指标。
该队列包括34名女性和20名男性，平均年龄为62±15岁。分形分析显示观察者间具有良好的可重复性（Kappa>0.70）。与良性脑膜瘤组相比，非典型脑膜瘤组的FDa和FDm均显著更高（P<.0001）。多变量逻辑回归模型具有统计学意义，P =.0001，AUC = 0.87。FDm大于1.31（优势比[OR]，12.30；P =.039）以及非颅底定位（OR，.052；P =.015）被确认为非典型表型的统计学显著预测指标。
术前MR图像的分形分析似乎是识别具有潜在侵袭性临床行为的脑膜瘤的一种可行的辅助诊断工具。
